Metadata-Version: 2.1
Name: numcertainties
Version: 0.0.0
Summary: uncertainty quantification for numerical computations
Home-page: https://github.com/APN-Pucky/numcertainties
Author: APN
Author-email: APN-Pucky@no-reply.github.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: uncertainties
Requires-Dist: numpy
Requires-Dist: jacobi
Requires-Dist: mcerp
Provides-Extra: dev
Requires-Dist: build ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: pytest-profiling ; extra == 'dev'
Requires-Dist: pytest-line-profiler-apn (>=0.1.3) ; extra == 'dev'
Requires-Dist: jupyterlab ; extra == 'dev'
Requires-Dist: pandas ; extra == 'dev'
Requires-Dist: ipython ; extra == 'dev'
Provides-Extra: docs
Requires-Dist: jupyter-sphinx ; extra == 'docs'
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Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
Requires-Dist: numpydoc ; extra == 'docs'
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# numcertainties

This project aims to unify the use of several error propagation libraries in python:

- <https://github.com/HDembinski/jacobi>
- <https://github.com/lebigot/uncertainties/>
- <https://github.com/tisimst/mcerp>
- (<https://github.com/tisimst/soerp>)

In future general differentiation routines could be used to get the jacobian similar to the `jacobi` package to get uncertainties from there:

- <https://pypi.org/project/numdifftools/>
- scipy
- (sympy for analytic derivatives i.e. as we store the operations we can do an anlytic derivative?, though eg. exp will be difficult)

TODO:
- p values / confidence
- handle mixed types of uncertainties
